Market adoption of wireless LAN (WLAN) technology has exploded, as users from a wide range of backgrounds and vertical industries have brought this technology into their homes, offices, and increasingly into the public air space. This inflection point has highlighted not only the limitations of earlier-generation systems, but the changing role WLAN technology now plays in people's work and lifestyles, across the globe. Indeed, WLANs are rapidly changing from convenience networks to business-critical networks. Increasingly users are depending on WLANs to improve the timeliness and productivity of their communications and applications, and in doing so, require greater visibility, security, management, and performance from their network.
As enterprises and other entities increasingly rely on wireless networks, the proper deployment and configuration of wireless access points in a wireless network environment becomes critical to performance and security. One problem with wireless networks is that they are complicated to configure effectively and have traditionally required wireless experts to appropriately deploy and manage. The installation of a WLAN typically involves the physical deployment of access points in one or more physical locations throughout a desired service region, the use of site surveys and/or other analysis tools to assess the radio-frequency (RF) coverage provided by the deployed access points, and the configuration of operational parameters for each access point to optimize operation of the wireless network. Furthermore, efficient operation of wireless networks usually requires regular monitoring and administration due to the dynamic nature of the RF environment in which the wireless access points operate.
Configuration of a wireless network is complicated because of the inherent attributes of RF propagation (typically in indoor environments), including multipath, interference, and other phenomena that affect signal propagation and, thus, WLAN performance. As discussed above, configuration of a wireless network involves setting, and subsequently adjusting, a variety of operational parameters. These parameters may include, for example, RF channels, frequency bands (e.g., IEEE 802.11a/b/g modes), transmit power, and receiver sensitivity.
During deployment of a wireless network, access points (APs) are physically installed in selected locations, depending on where users are expected or predicted to use their wireless devices. WLAN deployments may span hundreds to thousands of APs to provide wireless coverage and mobility services for a large user base associated with enterprises or wireless service providers offering wireless hotspots. After the APs are physically placed in their selected locations, a network administrator may then construct a model of the RF environment, including the location of walls and other obstructions, to assist in configuring one or more operational parameters for the APs. Site surveys and RF prediction can then be used to assess the expected RF coverage provided by the deployed access points. For example, the network administrator may manually conduct a site survey to assess the radio coverage and other performance attributes of the wireless infrastructure. During a site survey, the network administrator physically walks around selected locations or walk-about points with a site survey tool and determines the signal strength corresponding to each AP within the coverage area.
As discussed above, to ascertain the coverage and other performance attributes of a wireless network deployment, RF prediction can also be used to construct site-specific models of RF signal propagation in a given wireless network environment. RF predication can be used in combination with, or in lieu of, site surveys. RF prediction uses mathematical techniques, such as ray tracing, to model the effects of physical obstructions, such as walls, doors, windows, and the like, on RF signal propagation in a given environment. For example, S. Fortune, “Algorithms for Prediction of Indoor Radio Propagation,” Technical Memorandum, Bell Laboratories (1998), disclose various algorithms that can be used to predict radio signal propagation. Valenzuela et al., “Indoor Propagation Prediction Accuracy and Speed Versus Number of Reflections in Image-Based 3-D Ray-Tracing,” Technical Document, Bell Laboratories (1998), describe algorithms for modeling RF signal propagation in indoor environments. In addition, Rajkumar et al., “Predicting RF Coverage in Large Environments using Ray-Beam Tracing and Partitioning Tree Represented Geometry,” Technical Document, AT&T Bell Laboratories (1995), also disclose methods for predicting RF signal propagation in site specific environments.
With an RF model of the environment in which a WLAN is deployed, known processes and algorithms can be used to compute a suggested set of operational parameters for the access points of the WLAN, such as channel and transmit power assignments designed to optimize coverage and reduce interference. However, in known prior art systems, the construction of an RF model of a WLAN (either by site survey or RF prediction) typically involves the manual entry of a variety of data points, such as the location of the APs within the physical environment, antenna types, antenna gain, and sometimes the orientation of the antennas corresponding to the APs. Unfortunately, manual entry of this information is inconvenient, repetitive and error-prone, especially where there are a large number of APs.
As discussed above, due to the changing nature of an RF environment (such as changing or new sources of RF interference), a WLAN typically requires constant monitoring to ensure adequate performance. To monitor the wireless network, the network administrator may perform additional site surveys to assess the performance of the WLAN and/or the accuracy of the RF model used to configure the WLAN. The administrator may then use the data collected during the site survey to fine tune or reconfigure the wireless network. The optimization of a wireless network is difficult because of all of the considerations involved. Optimization of a wireless network is time consuming not only because of the inherent attributes of RF propagation, as discussed above, but also because it is an iterative process, as the multitude of measurements may become outdated as soon as the environment changes. For example, if equipment or furniture (e.g., a file cabinet) in a building is moved, the performance of the wireless network may change. Accordingly, when configuration of the wireless network becomes outdated, a new RF model (potentially involving additional site surveys and analysis) may be required.
In light of the foregoing, a need in the art exists for methods, apparatuses, and systems that facilitate automatic configuration of wireless networks. Embodiments of the present invention substantially fulfill this need.